1. Technical Field
The present invention relates to pharmacokinetic (PK) analysis.
2. Discussion of the Related Art
The diagnosis of breast cancer from Magnetic Resonance Imaging (MRI) data is a tough problem exacerbated by the fact that a malignant lesion often displays intensity patterns similar to benign tissues and other structures (such as the heart) in the field of view. However, malignant tissues differ from benign tissues in how Contrast Agents (CAs) flow in and leak out. These molecules affect the observed intensity patterns because they change the longitudinal relaxation times at the voxels in the image. Unlike their behavior with respect to intensity itself, malignant tissues display a characteristic pattern with regard to how much of the CA they take up, and also with regard to the rates of entry and wash-out of the CA. Dynamic Contrast-Enhanced (DCE) MRI uses this property to identify regions of interest. Pharmacokinetic (PK) analysis then aims to quantify the wash-in and wash-out of the CA towards differentiating malignant and benign lesions. The ideal goal of PK analysis in the context of breast MRI is to provide a framework where the kinetics of the CA within the tissue of interest can be quantitatively described, and compared across data sets from one or more patients and/or MR systems. However, current systems do not meet this requirement due to difficulties in the normalization that the system can perform on the input image data, which impairs the effectiveness of any population studies conducted.
Existing literature on PK analysis for breast MR can be categorized into two broad classes of models—compartmental and heuristic. The first class attempts to describe the microscopic view of the breast tissues as a set of compartments and then models the interaction between these compartments with respect to the entry and exit of the CA. Within this class, the models differ in the number of compartments they use to model the tissue and the equations that describe the interactions. Heuristic models attempt to model the wash-in and wash-out phenomena—as growing(/decaying) exponentials for example—and quantify the extents and rates of the same. Of the compartmental models, the Tofts model is the most commonly used. A comparative study of different PK models for DCE-MRI can be found, for example, in [R. Srikanchana, D. Thomasson, P. Choyke, and A. Dwyer. A comparison of pharmacokinetic models of dynamic contrast-enhanced MRI. CBMS 2004. Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems, 2004, pages 361-366, 2004]. The challenges in estimating the quantity of CA in the vascular space and the unsatisfactory normalization which impairs population studies are key issues that need to be addressed.